Comments (2)
Hi Anastasia,
thanks for using our code.
- There are two points: Firstly, did you try using the refinement for the realtime pipeline? I think it is not activated by default. You can do that by pressing 't'. Secondly, there could be several frames, where the assumption that the hand is closest object to the camera is violated due to noise, which then corrupts the results. This is not the case for the training/testing since we do offline preprocessing beforehand.
- Yes, looks good, should be around 8mm for both subjects on average.
- The file is NYU_network_prior.pkl and it is provided along with the other files. You don't have to load it in main_nyu_posereg_embedding.py#L145 , but loading the prior network in main_nyu_posereg_embedding.py#L158 is sufficient.
from deep-prior-pp.
Hi Markus,
Thank you so much! I have set tracking to true by pressing 't' and the accuracy became as high as in the paper.
Thanks again for sharing the code and for a prompt reply!
Best regards,
Anastasia
from deep-prior-pp.
Related Issues (20)
- the code be written in tensorflow HOT 2
- Estimate 3D hand pose in ASL Finger Spelling Dataset HOT 1
- Is there guide to install DepthSense SDK HOT 1
- Deep prior for RGB HOT 1
- use ICVLImporter and NYUImporter HOT 6
- reference point(center point) HOT 3
- The network for refining hand localizatoin HOT 1
- This is some woring with the code! HOT 2
- centers for custom dataset HOT 3
- I have a doubt about the hand detection algorithm. HOT 2
- Dataset issue HOT 1
- DHG-14/28 dataset center reference points
- About the joint refinement network? HOT 1
- Pretrained ICVL and NYU accuracy HOT 1
- about the openni2 and kinect HOT 1
- can it be trained without use icvl or nyu importer?
- can it be run without use icvl or nyu importer? HOT 5
- Could you provide Pretrain Model? HOT 1
- train/test split of the msra15 dataset HOT 5
- Could you provide more explanation about the input of the "RealtimeHandposePipeline" class HOT 1
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from deep-prior-pp.